Prof. Raffaele de Amicis Profile

Prof. Raffaele de Amicis

at Oregon State Univ

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Proceedings Article | 18 May 2020 Paper

A systematic literature review on dynamic cognitive augmentation through immersive reality: challenges and perspectives

Eric Prather, Arash Shahbaz Badr, Bruno Simões, Raffaele De Amicis

Proceedings Volume 11426, 114260G (2020) https://doi.org/10.1117/12.2563816

KEYWORDS: Virtual reality, Analytical research, Augmented reality, Cognition, Independent component analysis, Sensors, Manufacturing, Skin, Brain-machine interfaces, Cognitive modeling

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Rapid advances in immersive reality technologies have resulted in a vast quantity of research papers which generally include or attempt to apply it to human cognitive augmentation. Taking advantage of traditional psychological and physiological metrics for studying human cognition and behavior, researchers are applying various near-real time analysis techniques to modulate immersive experiences and influence the mental state of the user. Because of the variety of contributing sub-domains, there is little consensus as to any rigid paradigm for the knowledge being synthesized. In this paper, we conduct a systematic literature review to determine the state of the art of dynamic cognitive augmentation in immersive environments. Following a structured query of academic publications, we conduct an in-depth analysis of 104 papers from a sample of 538. We observed that roughly 66% of papers among this frontier apply methods best suited for exploratory purposes, limiting the overall extent to which conclusions can be drawn about immersive reality technology’s capability to augment human cognition. We further identify a pressing gap in the knowledge necessary for the effective application of immersive reality towards dynamic cognitive augmentation in practical industrial scenarios. We hope this work will influence academia, industry, and standards development organizations to extend the use of XR technology networked with biosensor-enabled intelligent cognitive assistants to enhance the effectiveness of hybrid human-machine systems.

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